Saturday 19 July 2014

Using SPSS

Using SPSS for Descriptive Statistics

SPSS is use to create histograms, frequency distributions, stem and leaf plots, Tukey box plots, calculate the standard measures of central tendency (mean, median, and mode), calculate the standard measures of dispersion (range, semi-interquartile range, and standard deviation / variance), and calculate measures of kurtosis and skewness. 



The Frequency Command
The frequencies command can be used to determine quartiles, percentiles, measures of central tendency (mean, median, and mode), measures of dispersion (range, standard deviation,  variance, minimum and maximum), measures of kurtosis and skewness, and create histograms. The command is found at Analyze | Descriptive Statistics | Frequencies (this is shorthand for clicking on the Analyze menu item at the top of the window, and then clicking on Descriptive Statistics from the drop down menu, and Frequencies from the pop up menu.



The frequencies dialog box will appear



Select the variable(s) that you want to analyze by clicking on it in the left hand pane of the frequencies dialog box. Then click on the arrow button  to move the variable into the Variables pane:



Be sure to select " Display frequency  tables" if you want a frequency distribution.  Specify which statistics you want to perform by clicking on the Statistics button. The Statistics dialog box will appear:



From the statistics dialog box, click on the desired statistics that you want to perform. To calculate a given percentile, click in the box to the left of percentile(s). Type in the desired percentile and click on the Add button. When you have selected all the desired statistics (e.g. mean, median, mode, standard deviation, variance, ragne, etc.), click on the Continue button.
Specify which chart you want to display by clicking on the Chart button. The chart dialog box will appear:




Click on the desired chart (usually Histogram) and click on the Continue button.

Click on OK in the frequencies dialog box. The SPSS Output Viewer will appear.
In the SPSS Output Viewer, you will see the requested statistics and chart. This is what the Statistics output looks like. It lists the requested measures of central tendency, measures of dispersion, measures of skewness and kurtosis, and the quartiles and percentiles.





The output has two columns. The left column names the statistic and the right column gives the value of the statistic. For example, the mean of this data is 1.26 (since your data set may be different, you may get a different value.)

The skewness measure is greater than 0 when the distribution is skewed.
The kurtosis measure is 0 for a normal distribution. Positive values imply a leptokurtic distribution, while negative values imply a platykurtic distribution.

If you scroll down, you will see the frequency distributions.


If you scroll down, you will see the histogram (or whatever chart you requested.)




The Descriptives Command

The descriptives command can be used to determine measures of central tendency (mean), measures of dispersion (range, standard deviation, variance, minimum and maximum), and measures of kurtosis and skewness. The command is found at Analyze | Descriptive Statistics | Descriptives (this is shorthand for clicking on the Analyze menu item at the top of the window, and then clicking on Descriptive Statistics from the drop down menu, and Descriptives from the pop up menu.



The descriptives dialog box will appear:



Select the variable(s) that you want to analyze by clicking on it in the left hand pane of the descriptives dialog box. Then click on the arrow button to move the variable into the Variables pane:



Specify which statistics you want to perform by clicking on the Options button. The Options dialog box will appear:



Select the statistics that you want by clicking on them (e.g. mean, standard deviation, variance, range, minimum, etc.). Then click on the Continue button. Click on the OK button in the Descriptives dialog box. The SPSS Output Viewer will appear with your results in it. The following is an example of the output:





The output gives the values of the requested statistics.


The Explore Command

The explore command can be used to determine measures of central tendency (mean and median), measures of dispersion (range, standard deviation, variance, minimum and maximum), measures of kurtosis and skewness, and prepare histograms, stem and leaf plots, and Tukey box plots. The command is found at Analyze | Descriptive Statistics | Explore:



The explore dialog box will appear:



Select the variable(s) that you want to analyze by clicking on it in the left hand pane of the explore dialog box. Then click on the top arrow button to move the variable into the Dependent List:



Specify which plots you want to prepare by clicking on the Plots button. The Plots dialog box will appear:



Select the plots that you want by clicking on them (e.g. Stem-and-leaf and histogram). Then click on the Continue button. Click on the OK button in the Explore dialog box. The SPSS Output Viewer will appear with your results in it. The following is an example of the output for the descriptive statistics:




The output gives the values of the requested statistics. If you scroll down, you will see the requested plots:







The Tukey box plot shows the first (bottom of box) and third (top of box) quartiles (equivalently the 25th and 75th percentiles), the median (the horizontal line  in the box), the range (excluding outliers and extreme scores) (the "whiskers" or lines that extend from the box show the range), outliers (a circle represents each outlier -- the number next to the outlier is the observation number.) An outlier is defined as a score that is between 1.5 and 3 box lengths away from the upper or lower edge of the box (remember the box represents the middle 50 percent of the scores). An extreme score is defined as a score that is greater than 3 box lengths away from the upper or lower edge of the box.

Saturday 12 July 2014

Importance of Statistics

Importance Of Statistics:


Most people who aren’t business majors or math majors often wonder what they need statistics for as it seems to be something only majors similar to those would need. However, statistics plays an important role in a great number of different fields, some of which  might not have expected. Here’s a list of fields that use statistics and why it’s important to each field.

The Role of Statistics in Business


 Statistics involves making decisions, and in the business world, a person often has to make a quick decision then and there.

Using statistics, a person can plan the production according to what the customer likes and wants, and can check the quality of the products far more efficiently with statistical methods. In fact, many business activities can be completed with statistics including deciding a new location, marketing the product, and estimating what the profit will be on a new product.

The Role of Statistics in Mathematics


It should seem obvious that statistics plays a key role in mathematics considering it’s a branch of applied mathematics. However, statistics is in more than just its own separate branch of math. A person can find statistical techniques in integration, differentiation, and algebra, and you can find those in statistics as well.


Much of math is based on probability and theories, and statistical methods help make those mathematical theories that much more accurate. Using averages, dispersions, and estimation allows to come up with conclusions that are closer to the real answer than just taking a wild guess. 

The Role of Statistics in Economics


Much of economics depends on statistics. Economists use statistics to collect information, analyze data, and test hypotheses. Relationships between supply and demand and imports and exports are found using statistical information. The same can be said for figuring out the inflation rate, the per capita income, and even the national income account. A good example of statistics and economics in the real world would be the Census Bureau and the information they collect and use to decide many other political items.

The Role of Statistics in Accounting


Accounting involves mostly basic arithmetic, but when it comes to creating accounting reports, statistics plays a key role. When balancing and checking accounts, exactness is very important, but when using those reports to decide how well the company is doing and the trends within the business.

The Role of Statistics in Banking



Banks use statistics for a great number of the services they offer. A bank works on the idea that someone will deposit their money and not withdraw all of it later on. They earn their profit by lending money to others with interest, and the money they use is the money other people deposit.


Bankers use statistical approaches to estimate the number of people who will be making deposits compared to the number of people requesting loans. A great example of statistics used in banking is the FDIC’s own quarterly publication.

The Role of Statistics in Management and Administration


A nation’s government runs on statistics. They use statistical data to make their decisions regarding any number of things. Most federal and provincial budgets are designed upon statistical data because it’s the most accurate data available when estimating expected expenditures and revenue.


Another great example of statistics in the government is figuring out whether or not to raise the minimum wage due to a rise in the cost of living. Statistical data gives the government the best idea regarding whether or not the cost of living will continue to rise.

The Role of Statistics in Astronomy


It is impossible to take out a ruler and measure the distance of the Earth from the sun. Unless, of course, you somehow manage to invent a suit that can survive the temperatures of the sun and design a ruler long enough to measure such a distance. However, it would likely take you a very long time to measure such a distance anyway.


Instead, astronomers use estimates and mathematical theories to devise their best guess to just how far items in the universe are away from each other. This is why when you read a news report that a star will likely be going supernova “any day now,” you have to understand that “any day now” could mean tomorrow, a year from now, or even ten thousand years from now.

The Role of Statistics in the Natural and Social Sciences

Biology, physics, chemistry, meteorology, sociology, communication, and even information technology all use statistics. For many of these categories, the use of statistics in that field involves collecting data, analyzing it, coming up with a hypothesis, and testing that hypothesis.
In biology, the use of statistics within that field is known as biostatistics, biometry, or biometrics. Biostatistics often involves the design of experiments in medicine, pharmacy, agriculture, and fishery. It also involves collecting, summarizing, and analyzing the data received from those experiments as well as the decided results. Medical biostatistics is a separate branch that deals mainly with medicine and health.
Physics uses probability theory and statistics dealing mainly with the estimation of large populations. In fact, the phenomenological results of thermodynamics were developed using the mechanics of statistics.
There are further examples of statistics in these sciences fields including analytical chemistry, which involves the presentation of problems in data analysis and demonstrating steps to solve them. Meteorology uses statistics in stochastic-dynamic prediction, weather forecasting, probability forecasting, and a number of other fields.
Sociology uses statistics to describe, explain, and predict from data received. Like many of the sciences, communication uses statistical methods to communicate data received. Information technology also uses statistics to predict particular outcomes.

Thursday 10 July 2014

Famous Statistician

Florence Nightingale David 


 
Florence Nightingale David, also known as F. N. David (23 August 1909 – 23 July 1993) was an English statistician, born in Ivington, Herefordshire, England. She was named after Florence Nightingale, who was a friend of her parents.

Florence Nightingale David, a great statistician and a fighter for increasing women’s roles in the sciences, began her career as a research assistant in Karl Pearson’s laboratory.   During World War II, she became an experimental officer and senior statistician for the Research and Experiments Department, and was scientific advisor on mines for the military.  David felt that the war gave women more opportunities and that conditions for them are now better because of it.  After serving as a lecturer and professor at University College for many years, in 1970 she was offered the chair of statistics at the University of California at Riverside. David read mathematics at Bedford College for Women in London. After graduation, she worked for the eminent statistician Karl Pearson at University College, London as his research student. She calculated the distribution of correlation coefficients, producing in 1938 her first book, Tables of the correlation coefficient.